near-ML Detector Research Articles

Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver

This paper presents a proposal for an architecture in FPGA for the implementation of a low complexity near maximum likelihood (Near-ML) detection algorithm for a multiple input-multiple output (MIMO) quadrature spatial modulation (QSM) transmission system. The proposed low complexity detection algorithm is based on a tree search and a spherical detection strategy. Our proposal was verified in the context of a MIMO receiver. The effects of the finite length arithmetic and limited precision were evaluated in terms of their impact on the receiver bit error rate (BER). We defined the minimum fixed point word size required not to impact performance adversely for n T transmit antennas and n R receive antennas. The results showed that the proposal performed very near to optimal with the advantage of a meaningful reduction in the complexity of the receiver. The performance analysis of the proposed detector of the MIMO receiver under these conditions showed a strong robustness on the numerical precision, which allowed having a receiver performance very close to that obtained with floating point arithmetic in terms of BER; therefore, we believe this architecture can be an attractive candidate for its implementation in current communications standards.

RF mirror media‐based space‐time block coded spatial modulation techniques for two time‐slots

Space-time block coded spatial modulation (STBC-SM) with cyclic structure (STBC-CSM) employs cyclical rotation of activated transmit antenna pairs to transmit Alamouti codewords taken from two different constellation sets to improve the spectral efficiency of STBC-SM. Furthermore, existing literature has shown that media-based modulation (MBM), which employs radio frequency (RF) mirrors demonstrate significant improvement in error performance of wireless systems. Hence, this study proposes the application of MBM to STBC-CSM and STBC-SM in the form of media-based STBC-CSM (MBSTBC-CSM) and MBSTBC-SM, respectively. The theoretical framework of the union-bound on average bit-error probability of M-QAM MBSTBC-CSM and MBSTBC-SM, for the maximum-likelihood (ML) detector is formulated and agrees well with Monte Carlo simulations. Furthermore, due to the large computational complexity of the ML detector, the authors propose a low-complexity near-ML detector for MBSTBC-CSM and MBSTBC-SM, which achieves a near-ML bit error rate performance with reduction in computational complexity.

A Low-Complexity Detection Scheme for Differential Spatial Modulation

Differential spatial modulation (DSM), which does not require the channel state information at the receiver, is an attractive alternative to its coherent counterpart. The optimal maximum-likelihood (ML) detector of the DSM system employs the classic block-by-block method for jointly detecting the activated antenna matrix (AM) and the modulation symbols, resulting in high computational complexity. In this letter, a low-complexity near-ML detector, which operates on a symbol-by-symbol basis, is proposed for the DSM scheme. Specifically, in each block, the index of the activated transmit antenna and modulation symbol in each time slot are first obtained, and then, these antenna indices are utilized to simply determine the index of the activated AM. Simulation results show that the proposed algorithm is capable of offering almost the same performance as that of the ML detector with more than 90% reduction in complexity.

Soft‐output space‐time block coded spatial modulation

Space-time block coded spatial modulation (STBC-SM) is a recently proposed multiple-input–multiple-output transmission scheme, which combines spatial modulation (SM) and Alamouti's space-time block code (STBC) in such a manner so as to retain the primary advantages of SM and STBC, while avoiding their drawbacks, resulting in an improved system error performance. In this study, the authors propose a soft-output maximum-likelihood (ML) detector (SOMLD) and a soft-output low-complexity near-ML detector (SOLCD) for STBC-SM transmission. Monte-Carlo simulations demonstrate that the error performance of the proposed schemes are able to closely match that of the hard decision ML detector for uncoded channel conditions, while significant improvement in error performance is demonstrated for coded systems. Furthermore, compared with SOMLD, SOLCD imposes a significantly lower computational complexity, while achieving near-ML error performance.

Reduced-Complexity Near-ML Detector for a Coded DSTTD-OFDM System

This letter introduces an efficient near-maximum likelihood (ML) detector for a coded double space-time transmit diversity-orthogonal frequency division multiplexing (DSTTD-OFDM) system. The proposed near-ML detector constructs a candidate vector set through a relaxed minimization method. It reduces computational loads from $\mathcal{O}(2|\mathcal{A}|^{2})$ to $\mathcal{O}(|\mathcal{A}|^{2})$, where $|\mathcal{A}|$ is the modulation order. Numerical results indicate that the proposed near-ML detector provides both almost ML performance and considerable complexity savings.

A VLSI 8 × 8 MIMO Near-ML Detector with Preprocessing

Multiple-input multiple-output (MIMO) systems are of significant interest due to their ability to increase the capacity of wireless communications systems, but for these to be useful they must also be practical for implementation in VLSI circuits. A particularly difficult part of these systems is the detector, where the optimal maximum-likelihood solution is desirable, but cannot be directly implemented due to its exponential complexity. This paper addresses this challenge and presents a digital circuit design for an 8×8 MIMO detection problem. A key feature is the integrated channel preprocessing unit, which performs the channel decomposition functions that are either omitted or performed "off-line" in other designs. The proposed device achieves near maximum likelihood bit error rate results at 57.6 Mbps. Other novelties include a high speed sorting mechanism and power saving features.

Performance analysis for MIMO systems with lattice-reduction aided linear equalization

Multi-input multi-output (MIMO) systems equipped with multiple antennas have well documented merits in combating fading and enhancing data rates. MIMO V-BLAST transmission is a widely adopted method to achieve high spectral efficiency and low-complexity implementation. When the maximum likelihood (ML) or near-ML detector is employed, receive diversity is collected for MIMO V-BLAST systems to enhance the performance. However, because of its exponential complexity, ML detector may be infeasible for practical systems when the number of antennas and/or the constellation size is large. On the other hand, linear equalizers have much lower complexity but come with inferior performance. In this paper, we analytically quantify the diversity order of linear detectors for MIMO V-BLAST systems. Then, we adopt low-complexity complex lattice-reduction (LR) aided linear equalizers for V-BLAST systems to improve the performance and prove that LR-aided linear equalizers collect the same diversity order as that exploited by the ML detector but with much lower complexity. Relative to the existing real LR-aided equalizers, we illustrate that the complex LR further reduces the complexity while keeping the same performance. Simulation results corroborate our theoretical claims.